DatriseAI-first ETL

Gong to Sisense Integration

AI-first ETL from Gong into Sisense. Governed entities, incremental sync, typed landing tables.

How Datrise loads Gong into Sisense

Datrise syncs Gong's calls, transcripts, deals, users, trackers, and call topics into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Gong: Revenue intelligence from recorded sales calls.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Gong entities map to Sisense

Gong entitySisense objectNotes
callsgong_callsid PK · custom fields → flattened columns for the cube
transcriptsgong_transcriptsid PK · linked to gong_calls
dealsgong_dealsid PK · linked to gong_calls
usersgong_usersid PK · linked to gong_calls

FAQ

How does Datrise handle Gong's custom fields in Sisense?

Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.

How does the Gong to Sisense sync stay up to date?

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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